Skip to main content
SpringerLink
Log in

Lost in Transmission: Investigating Filtering of COVID-19 Websites

  • Conference paper
  • First Online:
Financial Cryptography and Data Security (FC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12675))

Included in the following conference series:

Abstract

After the unprecedented arrival of the COVID-19 pandemic, the Internet has become a crucial source of essential information on the virus. To prevent the spread of misinformation and panic, many authorities have resorted to exercising higher control over Internet resources. Although there is anecdotal evidence that websites containing information about the pandemic are blocked in specific countries, the global extent of these censorship efforts is unknown. In this work, we perform the first global censorship measurement study of websites obtained from search engine queries on COVID-19 information in more than 180 countries. Using two remote censorship measurement techniques, Satellite and Quack, we collect more than 67 million measurements on the DNS and Application layer blocking of 1,291 domains containing COVID-19 information from 49,245 vantage points in 5,081 ASes. Analyzing global patterns, we find that blocking of these COVID-19 websites is relatively low—on average, 0.20%–0.34% of websites containing information about the pandemic experience interference. As expected, we see higher blocking in countries known for censorship such as Iran, China, and Kazakhstan. Surprisingly, however, we also find significant blocking of websites containing information about the pandemic in countries generally considered as “free” in the Internet space, such as Switzerland (DNS), Croatia (DNS), and Canada (Application layer). We discover that network filters in these countries flag many websites related to COVID-19 as phishing or malicious and hence restrict access to them. However, our investigation suggests that this categorization may be incorrect—most websites do not contain serious security threats—causing unnecessary blocking. We advocate for stricter auditing of filtering policies worldwide to help prevent the loss of access to relevant information.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Afroz, S., Fifield, D.: Timeline of Tor censorship (2007). http://www1.icsi.berkeley.edu/~sadia/tor_timeline.pdf

  2. Alexa Internet, Inc., Alexa Top 1,000,000 Sites. http://s3.amazonaws.com/alexa-static/top-1m.csv.zip

  3. Allcott, H., Gentzkow, M., Yu, C.: Trends in the diffusion of misinformation on social media. Research & Politics (2019)

    Google Scholar 

  4. Anonymous. Towards a comprehensive picture of the Great Firewall’s DNS censorship. In: Free and Open Communications on the Internet (FOCI) (2014)

    Google Scholar 

  5. Aryan, S., Aryan, H., Halderman, J.A.: Internet censorship in Iran: a first look. In: Free and Open Communications on the Internet (FOCI) (2013)

    Google Scholar 

  6. Brennen, J.S., Simon, F., Howard, P.N., Nielsen, R.K.: Types, sources, and claims of covid-19 misinformation. Reuters Institute (2020)

    Google Scholar 

  7. CheckPhish: Url Scanner to Detect Phishing in Real-time|CheckPhish. https://checkphish.ai/

  8. Cheng, C., Barceló, J., Hartnett, A.S., Kubinec, R., Messerschmidt, L.: Covid-19 government response event dataset. Nature Hum. Behav. 4, 756–768 (2020)

    Google Scholar 

  9. Cinelli, M., et al.: The covid-19 social media infodemic. arXiv preprint http://arxiv.org/abs/2003.05004 (2020)

  10. Citizen Lab. Block test list. https://github.com/citizenlab/test-lists

  11. Coppedge, M., et al.: V-dem codebook v. 10 (2020). https://www.v-dem.net/media/filer_public/28/14/28140582-43d6-4940-948f-a2df84a31893/v-dem_codebook_v10.pdf

  12. Crete-Nishihata, M., Dalek, J., Knockel, J., Lawford, N., Wesley, C., Zhou, M.: Censored contagion II: a timeline of information control on Chinese social media during COVID-19 (2020). https://citizenlab.ca/2020/08/censored-contagion-ii-a-timeline-of-information-control-on-chinese-social-media-during-covid-19/

  13. Dahir, A.L.: Internet shutdowns are costing African governments more than we thought. https://qz.com/1089749/internet-shutdowns-are-increasingly-taking-a-toll-on-africas-economies/

  14. Dainotti, A., et al.: Analysis of country-wide internet outages caused by censorship. In: ACM Internet Measurement Conference (IMC) (2011)

    Google Scholar 

  15. Durumeric, Z., Adrian, D., Mirian, A., Bailey, M., Halderman, J.A.: A search engine backed by Internet-wide scanning. In: Proceedings of the 2015 ACM SIGSAC Conference on Computer and Communications Security (2015)

    Google Scholar 

  16. Durumeric, Z., Wustrow, E., Halderman, J.A.: ZMap: fast internet-wide scanning and its security applications. In: USENIX Security Symposium (2013)

    Google Scholar 

  17. Ensafi, R., Winter, P., Mueen, A., Crandall, J.R.: Analyzing the great firewall of China over space and time. In: Proceedings on Privacy Enhancing Technologies (PETS) (2015)

    Google Scholar 

  18. Ferrara, E.: What types of COVID-19 conspiracies are populated by twitter bots? First Monday (2020)

    Google Scholar 

  19. FortiNet: Fortiguard labs web filter. https://fortiguard.com/webfilter

  20. Gharaibeh, M., Shah, A., Huffaker, B., Zhang, H., Ensafi, R., Papadopoulos, C.: A look at infrastructure geolocation in public and commercial databases. In: ACM Internet Measurement Conference (IMC) (2017)

    Google Scholar 

  21. Coronavirus search trends - google trends. https://trends.google.com/trends/story/US_cu_4Rjdh3ABAABMHM_en

  22. Jerreat, J.: Coronavirus the new scapegoat for media censorship, rights groups say (2020). https://www.voanews.com/press-freedom/coronavirus-new-scapegoat-media-censorship-rights-groups-say

  23. Johns Hopkins University: Coronavirus Resource Center. Covid-19 dashboard by the center for systems science and engineering (csse) at johns hopkins university (jhu). https://coronavirus.jhu.edu/map.html

  24. Kyaw, P.P., Xynou, M., Filastò, A.: Myanmar blocks “fake news” websites amid covid-19 pandemic. https://ooni.org/post/2020-myanmar-blocks-websites-amid-covid19/

  25. Lachapelle, J., Lührmann, A., Maerz, S.F.: An update on pandemic backsliding: Democracy four months after the beginning of the covid-19 pandemic. Policy Brief, V-Dem Institute (2020)

    Google Scholar 

  26. MacKinnon, R.: China’s censorship 2.0: how companies censor bloggers. First Monday (2009)

    Google Scholar 

  27. MaxMind. https://www.maxmind.com/

  28. Ofcom: Half of UK adults exposed to false claims about coronavirus (2020). https://www.ofcom.org.uk/about-ofcom/latest/media/media-releases/2020/half-of-uk-adults-exposed-to-false-claims-about-coronavirus

  29. Palo Alto Networks. Test a site. https://urlfiltering.paloaltonetworks.com/

  30. PC Magazine: Phishing attacks increase 350 percent amid covid-19 quarantine. https://in.pcmag.com/privacy/135635/phishing-attacks-increase-350-percent-amid-covid-19-quarantine

  31. Pearce, P., Ensafi, R., Li, F., Feamster, N., Paxson, V.: Augur: internet-wide detection of connectivity disruptions. In: IEEE Symposium on Security and Privacy (S&P), May 2017

    Google Scholar 

  32. Pearce, P., et al.: Global measurement of DNS manipulation. In: USENIX Security Symposium (2017)

    Google Scholar 

  33. Peng, P., Yang, L., Song, L., Wang, G.: Opening the blackbox of virustotal: analyzing online phishing scan engines. In: ACM Internet Measurement Conference (IMC) (2019)

    Google Scholar 

  34. Radu, R.: Fighting the ‘infodemic’: legal responses to COVID-19 disinformation. Social Media+Society (2020)

    Google Scholar 

  35. Ramesh, R., et al.: Decentralized control: a case study of Russia. In: Proceedings of the Network and Distributed System Security Symposium (NDSS) (2020)

    Google Scholar 

  36. Reporters Without Borders: Middle east governments clamp down on coronavirus coverage (2020). https://rsf.org/en/news/middle-east-governments-clamp-down-coronavirus-coverage

  37. University of Oregon Route Views Project. www.routeviews.org

  38. Ruan, L., Knockel, J., Crete-Nishihata, M.: Censored contagion: how information on the coronavirus is managed on Chinese social media (2020). https://citizenlab.ca/2020/03/censored-contagion-how-information-on-the-coronavirus-is-managed-on-chinese-social-media/

  39. Scott, W., Anderson, T., Kohno, T., Krishnamurthy, A.: Satellite: joint analysis of CDNs and network-level interference. In: USENIX Annual Technical Conference (ATC) (2016)

    Google Scholar 

  40. SeleniumHQ Browser Automation. www.selenium.dev

  41. Singh, L., et al.: A first look at COVID-19 information and misinformation sharing on Twitter (2020)

    Google Scholar 

  42. Sundara Raman, R., Evdokimov, L. , Wustrow, E., Halderman, A., Ensafi, R.: Investigating large scale HTTPS interception in Kazakhstan. In: Internet Measurement Conference (IMC). ACM (2020)

    Google Scholar 

  43. Sundara Raman, R., Shenoy, P., Kohls, K., Ensafi, R.: Censored planet: an internet-wide, longitudinal censorship observatory. In: ACM SIGSAC Conference on Computer and Communications Security (CCS) (2020)

    Google Scholar 

  44. Sundara Raman, R., et al.: Measuring the deployment of network censorship filters at global scale. In: Network and Distributed System Security Symposium (NDSS) (2020)

    Google Scholar 

  45. The Tor Project. OONI: Open observatory of network interference. https://ooni.torproject.org/

  46. University of Toronto. Citizen Lab. https://citizenlab.ca/

  47. Vallina, P., et al.: Mis-shapes, mistakes, misfits: an analysis of domain classification services. In: ACM Internet Measurement Conference (IMC) (2020)

    Google Scholar 

  48. VanderSloot, B., McDonald, A., Scott, W., Halderman, J.A., Ensafi, R.: Quack: scalable remote measurement of application-layer censorship. In: USENIX Security Symposium (2018)

    Google Scholar 

  49. Vyas, A., Sundara Raman, R., Ceccio, N., Lutscher, P.M., Ensafi, R.: Investigating filtering of COVID-19 websites (2020). https://censoredplanet.org/covid

  50. WatchGuard. See a site’s content category. https://www.watchguard.com/help/docs/help-center/en-US/Content/en-US/Fireware/services/webblocker/site_categories_see_websense_c.html

  51. Winter, P., Lindskog, S.: How the great firewall of china is blocking tor. In: Free and Open Communications on the Internet (FOCI) (2012)

    Google Scholar 

  52. Wiseman, J.: European media freedom suffers under COVID-19 response (2020). https://ipi.media/european-media-freedom-suffers-covid-19-response/

  53. World Health Organization: Coronavirus disease (COVID-19) pandemic (2020). https://www.who.int/emergencies/diseases/novel-coronavirus-2019

  54. Xu, X., Mao, Z.M., Halderman, J.A.: Internet censorship in China: where does the filtering occur? In: International Conference on Passive and Active Network Measurement (PAM) (2011)

    Google Scholar 

  55. Zhong, R., Mozur, P., Kao, J., Krolik, A.: No ‘Negative’ news: how China censored the coronavirus (2020). https://www.nytimes.com/2020/12/19/technology/china-coronavirus-censorship.html

  56. Zittrain, J., Edelman, B.: Internet filtering in China. IEEE Internet Comput. 7(2), 70–77 (2003)

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the shepherd Philipp Winter and the reviewers for their constructive feedback. We also thank Prerana Shenoy for her help with data analysis. This work was supported in part by research credits from Google.

Author information

Authors and Affiliations

  1. University of Michigan, Ann Arbor, USA

    Anjali Vyas, Ram Sundara Raman, Nick Ceccio & Roya Ensafi

  2. University of Oslo, Oslo, Norway

    Philipp M. Lutscher

Authors
  1. Anjali Vyas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ram Sundara Raman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nick Ceccio
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Philipp M. Lutscher
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Roya Ensafi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roya Ensafi .

Editor information

Editors and Affiliations

  1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Nikita Borisov

  2. KU Leuven, Leuven, Belgium

    Claudia Diaz

Appendices

Appendix 1

Classifier and Manual Categorizations. Table 5 shows the results of the categorization of the 46 domains blocked by the Fortinet filter in Switzerland and Croatia using different categorization tools.

Table 5. URL Classifier and Manual Categories—NTE stands for Navigation Time Exceeded, E stands for error and NC stands for not categorized.
Full size table

Appendix 2

Search Engine Crawl Keywords. Table 6 shows the list of prefix and suffix combinations used to construct the keywords used for our search engine crawls.

Table 6. Keyword permutations used for search engine crawls—Three terms (corona virus, covid virus, and covid-19 virus) are excluded from the table.
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2021 International Financial Cryptography Association

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vyas, A., Sundara Raman, R., Ceccio, N., Lutscher, P.M., Ensafi, R. (2021). Lost in Transmission: Investigating Filtering of COVID-19 Websites. In: Borisov, N., Diaz, C. (eds) Financial Cryptography and Data Security. FC 2021. Lecture Notes in Computer Science(), vol 12675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-64331-0_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-64331-0_22

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-64330-3

  • Online ISBN: 978-3-662-64331-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation